As AI agents move from experimentation into real enterprise workflows, identity security is being forced to catch up.
Okta’s latest announcement lays out a framework for what it calls a “secure agentic enterprise,” along with a new platform, Okta for AI Agents, expected to be generally available in April 2026.
Most identity systems were designed around human users. You log in, you get access, and your actions are relatively predictable. AI agents don’t behave that way. They can act autonomously, spawn other agents, access multiple systems, and execute workflows continuously. That creates a different kind of risk surface.
The data behind this shift is already showing up. According to Okta, 88% of organizations report suspected or confirmed AI agent security incidents, yet only 22% treat agents as identity-bearing entities. That gap matters. If agents aren’t treated like identities, they fall outside the controls that security teams rely on to manage access, monitor behavior, and respond to threats.
Ely Kahn, CPO, Okta Platform, told MSSP Alert, “Organizations want to move quickly and leverage AI agents across their business functions, but they can’t afford to sacrifice security along the way. The old adage in security is that you can’t defend what you can’t see, and that rings true for agentic AI. The blueprint is designed to guide organizations on their journeys to becoming secure agentic enterprises. By following the framework, security and IT leaders can maintain control and visibility over their agents while quickly responding if risk changes.”
The three questions shaping agentic security
Okta’s framework centers on three practical questions: Where are my agents? What can they connect to? What can they do?
These aren’t abstract concerns. They map directly to visibility, access control, and enforcement—the same pillars that define identity security today, but applied to non-human actors.
The difference is scale and speed. Agents can operate across systems faster than traditional controls can keep up, which means identity decisions need to happen continuously, not just at login.
From users to “non-human identities”
One of the more important shifts in this announcement is how Okta is positioning AI agents as first-class identities. That includes registering agents in a central directory, assigning ownership and lifecycle management, and applying policies similar to human users.
This move aligns with a broader trend already underway in security: the rise of machine identities. But AI agents take it further because they are not just passive systems. They actively make decisions and initiate actions.
For security teams, this changes how identity governance works. It’s no longer just about who has access, but also about what autonomous systems are doing with that access in real time.
The “control plane” approach to agent access
Another key piece is the idea of a centralized control layer for how agents interact with tools, APIs, and data. Okta’s Agent Gateway and API access controls aim to act as that layer, enforcing least-privilege access and logging interactions for audit.
This matters because AI agents often rely on chaining tools together. A single workflow might touch multiple systems, each with its own permissions model. Without centralized control, visibility breaks down quickly. For enterprises, this becomes less about securing individual applications and more about securing the pathways between them.
The importance of a “kill switch”
One of the more practical features in the framework is the ability to revoke an agent’s access instantly across systems. In a human context, revoking access is already a critical control. With AI agents, the stakes are higher because of how quickly they can act.
Ely Kahn explains how this works in practice: “The Universal Logout for AI Agents feature is designed to instantly revoke an agent’s access to the enterprise ecosystem. It works by effectively preventing a rogue AI agent from requesting access to any downstream service.”
That shift toward immediate containment reflects a broader reality. Response time is shrinking. Security teams don’t just need alerts, they need the ability to act across systems at once.
Shadow agents are the new shadow IT
Shadow IT has been a persistent issue for years. AI agents are now creating a similar problem, but with more autonomy and less visibility. Employees can spin up agents without going through IT, connect them to enterprise tools, and set them loose on workflows.
Kahn describes the challenge this creates: “Shadow agents pose a risk, not necessarily because of intent, but because they are spun up without proper visibility, governance, and security controls. Just like with shadow IT, the concerns arise when you don’t have adequate controls in place.
The problem is growing exponentially as organizations struggle to keep up with the democratization of agent creation, which allows any employee to provision a digital worker. Real-time detection is critical because it lets organizations know the exact moment an agent gains access to sensitive resources. Organizations first need discovery capabilities to keep pace with the rapid deployment we’re seeing across industries, and then the ability to prioritize and remediate risks.”
This highlights a practical issue many teams are already facing. It’s not just about securing known systems. It’s about finding what’s already running outside formal oversight.
What this means for enterprises and partners
For enterprises, the takeaway is straightforward: AI adoption is introducing a new identity layer that existing tools weren’t built to handle. Security teams will need visibility into all agents, including unsanctioned ones, continuous authorization instead of one-time authentication, centralized control over agent interactions, and real-time response mechanisms.
For MSPs and MSSPs, this opens up a new service category. Managing agent identities, governance, and risk will likely become part of broader identity and access management offerings. There’s also a near-term reality. Many organizations are already deploying agents without structured controls in place. That creates immediate demand for discovery, governance, and enforcement.
Okta is how reframing identity. Instead of being a front-door control, identity becomes a runtime system that evaluates actions continuously. That aligns with how AI agents actually operate. As agentic AI moves deeper into enterprise workflows, this model will likely become standard. The question is how quickly organizations can adapt their identity strategies to keep up.
